Our Opinion
Our Opinion
Embedded Files
It is easy to get excited about the new technological possibilities, but much less easy to make their implementation successful. The pitfalls are unsurprisingly not new, "plus ca change, plus c'est la meme chose"
Even today, when everyone and the mums of many are into GenAI, different people understand different things, and especially understand what can be done with different tools differently. This is one of the most common causes for failure in AI projects. It is important that everyone speaks the same language or at worse there are people who can translate the different languages. Part 4 of this series will talk about the former, this is about the translators and ensuring alignment in the organisation to be poised for success
The old saying "Garbage in, Garbage out" is still relevant today; Analytics/ML/AI/Gen AI depend more and more heavily on having high volume of relevant data. Add to this issues around biases, and it is clear why having the right data in terms of quality and quality is crucial for success.
Analytics is meant to help make life easier, whether that be in business or not. Hence the aim of analytics should be related to KPIs that matter to the organisation. However, very often, people are distracted by the desire to obtain the shiniest new toy. This, for many reasons, is not a path that leads to success for an organisation
To start an organisation appreciating and knowing how to use data and/or AI, what are some questions to consider
Many people see GenAI as a panacea, to end all ills in the world. However, this is far from the current situation. Furthermore, for organisations that have limited resoruces, it is necessary to choose when and where analytical/ML/AI resources should be allocated. The problems need to be doable and worth doing.
Having a data culture is not just having 1 or 2 departments using data to make business decisions. It takes a lot to become a data literate and finally data driven organisation.
So, how do we get everyone to live and breathe data everyday?
A chart of worth a 1000 words, but the key is to know the audience you are going for and your own intention: do you want to inform, create discussion...?
Report abuse